Real time audience forecasting

ABSTRACT

A system, method, apparatus and processor readable media are described for real-time prediction of an advertising audience volume through analysis of historical audience data, and tuning of the predicted audience volume. Embodiments enable a user to specify a query for audience volume prediction. Such a query may be a Boolean combination of various audience categories. A time range may be determined that indicates the amount of historical data that is to be analyzed to make the audience volume prediction in real time. Employing the user-specified query, an audience volume prediction may be provided for a future time period, based on an analysis of retrieved historical audience data for the time range. Embodiments may also enable a user to tune the predicted audience volume through modification of the query through one or more iterations.

FIELD OF ART

The present invention is directed to managing an exchange ofinformation, and more particularly, to using historical advertisingaudience data to predict in real time a future audience volume based ona user-specified query.

BACKGROUND

Information regarding users of web sites (e.g. a consumer or potentialconsumer) is often a hidden and fragmented entity on the web. In somesituations, an advertiser or web publisher may not have access toinformation for one or more web users' behavior on various sites on theweb. Further, an advertiser may not be able to predict in real-time theavailable size of the target audience for a proposed data and/oradvertising campaign. It is with respect to these considerations andothers that the present invention is directed.

BRIEF DESCRIPTION OF THE DRAWINGS

Non-limiting and non-exhaustive embodiments of the present invention aredescribed with reference to the following drawings. In the drawings,like reference numerals refer to like parts throughout the variousfigures unless otherwise specified.

For a better understanding of the present invention, reference will bemade to the following Detailed Description Of The Embodiments, which isto be read in association with the accompanying drawings, wherein:

FIG. 1 illustrates an example of a system in which embodiments of theinvention may be practiced;

FIG. 2 illustrates an example of a mobile device that may be included ina system implementing embodiments of the invention;

FIG. 3 illustrates an example of a network device that may be includedin a system implementing embodiments of the invention;

FIG. 4 illustrates a logical flow diagram generally showing anembodiment of a process for tuning a predicted audience volume;

FIG. 5 illustrates a logical flow diagram showing an embodiment of aprocess for providing an audience volume prediction;

FIG. 6 illustrates a logical flow diagram showing an embodiment of aprocess for tuning of a query for audience volume prediction; and

FIG. 7 illustrates an example user interface for audience volumeprediction.

DETAILED DESCRIPTION OF EMBODIMENTS

The present invention now will be described more fully hereinafter withreference to the accompanying drawings, which form a part hereof, andwhich show, by way of illustration, specific exemplary embodiments bywhich the invention may be practiced. This invention may, however, beembodied in many different forms and should not be construed as limitedto the embodiments set forth herein; rather, these embodiments areprovided so that this disclosure will be thorough and complete, and willfully convey the scope of the invention to those skilled in the art.Among other things, the present invention may be embodied as methods,systems, media or devices. Accordingly, the present invention may takethe form of an entirely hardware embodiment, an entirely softwareembodiment or an embodiment combining software and hardware aspects. Thefollowing detailed description is, therefore, not to be taken in alimiting sense.

Throughout the specification and claims, the following terms take themeanings explicitly associated herein, unless the context clearlydictates otherwise. The phrase “in one embodiment” as used herein doesnot necessarily refer to the same embodiment, though it may.Furthermore, the phrase “in another embodiment” as used herein does notnecessarily refer to a different embodiment, although it may. Thus, asdescribed below, various embodiments of the invention may be readilycombined, without departing from the scope or spirit of the invention.

In addition, as used herein, the term “or” is an inclusive “or”operator, and is equivalent to the term “and/or,” unless the contextclearly dictates otherwise. The term “based on” is not exclusive andallows for being based on additional factors not described, unless thecontext clearly dictates otherwise. In addition, throughout thespecification, the meaning of “a,” “an,” and “the” include pluralreferences. The meaning of “in” includes “in” and “on.”

For example embodiments, the following terms are also used hereinaccording to the corresponding meaning, unless the context clearlydictates otherwise.

As used herein, the term audience generally refers to a collection ofpeople who have exhibited in the past or are likely to exhibit in thefuture a set of online or offline behaviors and actions, have otherwisedirectly or indirectly communicated and/or exhibited a predispositiontowards or predilection for certain products, events, or entities,and/or have directly or indirectly indicated their affinity, inclusionor exclusion in certain groups (e.g. demographic) or categories. Lack ofbehavior and negative affinity could also be used as defining qualitiesof an audience. Such people may be web users who have exhibited certainonline behavior (e.g. browsing, search, purchase, Really SimpleSyndication (RSS) feed, social network activity, forum posting, and thelike).

The term audience may also refer to or incorporate a set of users whohave visited a particular website or set of websites. Examples ofaudience of this type include a) people who visited a specific onlinebookstore website, and b) women interested in green technology whovisited three pre-specified automobile comparison websites sometime inthe last month. The latter example combines demographic, site-visiting,and in-market audience components. Some embodiments may enable the userrequesting the audience forecast to issue queries related to their ownwebsite(s) (e.g. for retargeting purposes). For example, an audience maybe defined as women between the ages of 30 and 50, who are in market forEuropean travel, and who have visited my website A but not my website Bin the last 60 days.

In some embodiments, various audience components may be pertinent to anadvertising campaign, data modeling, audience analysis and optimization,and the like.

The term audience may also refer to the collection of people who see,experience, or are otherwise exposed to or potentially exposed to acampaign such as an advertising campaign, a promotional campaign, aninformational campaign, or the like. Such people may be web users whomay experience a campaign through advertisements placed on web sites orother web services. Such people may also be consumers who may be exposedto advertising through virtually any medium, including but not limitedto television, radio, print, physical displays, and the like. As usedherein, person is a broad term that encompasses an individual who may bepotentially exposed to a campaign through any media. A person may alsobe referred to as a consumer, which is meant broadly as a person who maypurchase, express an interest in, or otherwise to exposed to informationregarding a good and/or service for sale or otherwise provided at anytime during the past, present and/or future.

As used herein, the term audience volume generally refers to a number ofpeople in an audience. In some embodiments, audience volume may be anexact number of individual people. However, the invention is not solimited. In some embodiments, audience volume may be a more or lessapproximate count of the number of people in an audience, estimated byvirtually any estimation process.

As used herein, the term category generally refers to a subject or atopic of data for people in an audience. For example, data for a personwho purchased an SUV may be associated with a category of “SUV consumer”and/or broader categories of “automobile consumer” or “light truckconsumer.” Further, categories may be associated with broad categorytypes. For example, categories related to particular markets for goodsand/or services may be classified into a market or in-market type ofcategory. Demographic type categories may include categories related tovirtually any demographic statistic, including but not limited to ageand gender of a person. Location type categories may be related togeographical location definitions of varying scope. For example,location type categories may include “United States residents”, “westcoast U.S. residents”, “California residents”, “Los Angeles Countyresidents”, “Burbank residents”, and so forth. Seasonal category typesmay include categories related to particular times of year, seasons,periods of time, and the like. For example, data related to useractivities during the winter may be classified in a winter category.Visitorship categories may include categories related to particularvisits to certain websites, subsections of websites, collections ofwebsites, classes and/or categories of websites, and the like. Forexample, users who have visited www.abcdef.com may be classified as“visitors to the ABCDEF website”. Other types of categories may besupported by embodiments without departing from the scope or spirit ofthe claimed invention.

As used herein, the term user generally refers to a person who is actingas a user of the claimed invention, for example a user who is specifyinga query for audience volume prediction, receiving the results of theaudience volume prediction, and/or modifying the query to tune theaudience volume prediction. In some embodiments, the user may be a databuyer seeking an audience volume prediction prior to launching anadvertising campaign to target a particular audience.

As used herein, the term “time period” may generally refer to either acontiguous or non-contiguous period of time. A time period may bespecified in terms of specific time(s) of day (e.g. 1:00 pm), generaltime(s) of day (e.g. afternoon), date(s), day(s) of the week (e.g.Tuesday), types of day(s) of the week (e.g. weekday, weekend), month(s)of the year, season(s) of the year, holiday(s), holiday season(s), andthe like. A time period may be a specified range of time (e.g. from dateX to date Y), a span of time immediately prior to or before a particulardate (e.g. the month prior to date X), or a span of time generally priorto a particular date (e.g. any month prior to date X). A time period maybe non-contiguously defined as omitting dates (e.g. the month prior todate X except for weekend days). In general, time period as used hereinmay encompass virtually any description of time.

Briefly stated, embodiments of the invention are directed towardreal-time prediction of an advertising audience volume (e.g. audienceforecasting) through analysis of historical audience data, and tuningthereof to predict audience volume. Some embodiments may enable a userto specify a query to define an audience for audience volume prediction.Such a query may be a Boolean combination of various audiencecategories. Further a time range may be determined that indicates theamount (e.g. the last week, last month, last year, and the like) ofhistorical data that is to be analyzed to make the audience volumeprediction. Employing the user-specified query, an audience volumeprediction may be provided for a future time period, based on ananalysis of retrieved historical audience data for the time range.Embodiments may also enable a user to tune the predicted audience volumeand/or the desired audience definition through modification of the queryduring one or more iterations, until the user is satisfied with thepredicted audience volume. Parallel processing of data retrieval and/oranalysis of data by a cluster of servers may enable predictions ofaudience volume to be provided in real time.

As an example of iterative audience tuning, a user may specify anoriginal query of “online consumers who are female, interested in LuxuryCars, and live in the Southern U.S.” The original query may yield anaudience volume (e.g. inventory) of two million persons. The user maythen tune the query by changing the location criterion in the originalquery, for example to “ . . . in the Southern OR Western U.S. AND not inCalifornia OR Alabama.” This tuned query may yield a different audiencevolume of four million persons. Such tuning is described further hereinwith regard to FIGS. 4-7.

In some embodiments, the predicted audience volume may be considered anaudience inventory, such that the audience volume prediction may be usedto sell audience data to a potential customer such as an advertiser orother data buyer for a future advertising, promotional, and/orinformational campaign. However, the invention is not so limited. Insome embodiments, an audience volume prediction may be employed formarket research and/or other purposes that are not directly commercialin nature.

Example Operating Environment

FIG. 1 illustrates an embodiment of a system in which embodiments of thepresent invention may operate. However, not all of the components in thedepicted system may be required to practice the invention, andvariations in the arrangement and type of the components may be madewithout departing from the spirit or scope of the invention.

As shown in the figure, system 100 includes client devices 102-103,mobile device (e.g. mobile client device) 104, network 120, wirelessnetwork 130, one or more audience volume prediction servers 106, one ormore audience correlation servers 108, load balancers 110 and 112, datastorage 114, content server 116 and data buyer server 118. Network 120is in communication with and enables communication between each of theelements of system 100. Wireless network 130 further enablescommunication with wireless devices such as mobile device 104.

Client devices 102-104 may include virtually any computing devicecapable of receiving and sending a message over a network, and/or to andfrom another computing device. The set of such devices may includedevices that typically connect using a wired communications medium suchas personal computers, multiprocessor systems, microprocessor-based orprogrammable consumer electronics, network PCs, and the like. Further,client devices 102-104 may include any device that is capable ofconnecting via a wireless communication medium such as a personaldigital assistant (PDA), pocket PC, wearable computer, portablecomputing device, mobile computing device, cell phones, smart phones,pagers, walkie talkies, radio frequency (RF) devices, infrared (IR)devices, citizen band (CB) radio devices, or any other device that isequipped to communicate over a wireless communication medium. Clientdevices may also include integrated devices combining one or more of thepreceding devices and/or elements of one or more of the precedingdevices.

Each client device within client devices 102-104 may include a browserapplication that is configured to send, receive, and display web pages,and the like. The browser application may be configured to receive anddisplay graphics, text, multimedia, and the like, employing virtuallyany web based language, including, but not limited to StandardGeneralized Markup Language (SMGL), such as HyperText Markup Language(HTML), extensible markup language (XML), a Handheld Device MarkupLanguage (HDML), such as Wireless Markup Language (WML), WMLScript,JavaScript, and the like. Client devices 102-104 may further include amessaging application configured to send and/or receive a messageto/from another computing device employing another mechanism, including,but not limited to instant messaging (IM), email, Short Message Service(SMS), Multimedia Message Service (MMS), internet relay chat (IRC),mIRC, Jabber, and the like.

Network 120 is configured to couple one computing device to anothercomputing device, enabling them to communicate. Network 120 is enabledto employ any form of computer readable media for communicatinginformation from one electronic device to another. Also, network 120 mayinclude a wireless interface, and/or a wired interface, such as theInternet, in addition to local area networks (LANs), wide area networks(WANs), direct connections, such as through a universal serial bus (USB)port, other forms of computer-readable media, or any combinationthereof. On an interconnected set of LANs, including those based ondiffering architectures and protocols, a router, switch and/or othernetwork appliance may act as a link between LANs, enabling messages tobe sent from one to another. Also, communication links within LANstypically include twisted wire pair or coaxial cable, whilecommunication links between networks may utilize analog telephone lines,full or fractional dedicated digital lines including T1, T2, T3, and T4,Digital Signal level 3 (DS3), Optical Carrier 3 (OC3), OC12, OC48,Asynchronous Transfer Mode (ATM), Integrated Services Digital Networks(ISDNs), Digital Subscriber Lines (DSLs), wireless links includingsatellite links, or other communications links known to those skilled inthe art. Furthermore, remote computers and other related electronicdevices could be remotely connected to either LANs or WANs via a modemand temporary telephone link. Network 120 is constructed for use withvarious communication protocols and technologies, including transmissioncontrol protocol/internet protocol (TCP/IP), user datagram protocol(UDP), a wireless application protocol (WAP), global system for mobilecommunications (GSM), code division multiple access (CDMA), timedivision multiple access (TDMA), general packet radio service (GPRS),ultra wide band (UWB), IEEE 802.16 Worldwide Interoperability forMicrowave Access (WiMax), and the like. In essence, network 120 includesany communication method by which information may travel between thevarious devices of system 100. Network 120 may further include one ormore network management devices, which may include network providers,load balancers, application managers, or the like. Network managementdevices may manage communication sessions, tag communication traffic,place data cookies on client devices, and perform other networkmanagement operations.

Wireless network 130 may include wireless interfaces, wireless forms ofcommunication media, and/or wireless links such as cellular networks,satellite links. These may include wireless application protocol (WAP),global system for mobile communications (GSM), code division multipleaccess (CDMA), time division multiple access (TDMA), general packetradio service (GPRS), ultra wide band (UWB), IEEE 802.16 WorldwideInteroperability for Microwave Access (WiMax), and the like.

The media used to transmit information in communication links asdescribed above may generally include any media that can be accessed bya computing device. Such computer-readable media may includenon-transitory media such as computer readable storage media, which alsomay be referred to as processor readable storage media.Computer-readable media may also include transitory wired and/orwireless communication media, or any combination thereof. Additionally,computer-readable media typically embodies computer-readableinstructions, data structures, program modules, or other data. Such datamay be stored on computer readable storage media. Such data may also becommunicated through communication media in a modulated data signal suchas a carrier wave, data signal, or other transport mechanism andincludes any information delivery media. The terms “modulated datasignal,” and “carrier-wave signal” includes a signal that has one ormore of its characteristics set or changed in such a manner as to encodeinformation, instructions, data, and the like, in the signal. By way ofexample, communication media includes wireless media such as fluids orspace for acoustic, RF, infrared, and other wireless signals, and wiredmedia such as twisted pair, coaxial cable, fiber optics, wave guides,and other wired media.

Audience volume prediction server(s) 106, audience correlation server(s)108, content server 116, and/or data buyer server 118 may comprisemultiple computing devices, components of a single computing device, ora single device with multiple software features. In some embodiments,audience volume prediction server(s) 106 and/or audience correlationserver(s) 108 may comprise a cluster of server, such that the audiencevolume prediction and audience correlation functionality is shared amongthe servers of the cluster in a load-balanced and/or parallelprocessing. In some embodiments, system 100 may include load balancers110 and 112 or other network devices that manage the load balancing oftasks among audience volume prediction server(s) 106 and/or audiencecorrelation server(s) 108 respectively.

In some embodiments, audience volume prediction server(s) 106 and/oraudience correlation server(s) 108 may use external data storage 114 forstoring audience data used for audience volume prediction and/oraudience correlation. In some embodiments, audience volume predictionserver(s) 106 and/or audience correlation server(s) 108 may use internaldata storage for storing audience data.

Content server 116 may provide content such as web sites, onlinejournals (e.g., blogs), photos, reviews, online services such asmessaging, search, news, shopping, advertising, travel services, orvirtually any other content and/or services. While providing suchcontent or services, content server 116 may gather information aboutpersons who access the provided content (e.g., web users, consumersand/or potential consumers), including but not limited to informationsuch as products viewed or purchased, services viewed or purchased,articles read, content searches and the like. The gathered informationmay be collected, stored, correlated, or otherwise analyzed at anexchange server (not shown in FIG. 1). Such an exchange server andexchange service is described further in U.S. patent application Ser.No. 12/399,796, titled EXCHANGE FOR TAGGED USER INFORMATION WITHSCARCITY CONTROL, hereby incorporated by reference. Briefly, an exchangeserver may organize or reorganize the consumer information collectedfrom one or more content servers such as content server 116. An exchangeserver may facilitate the selling or providing of the collected consumerinformation to one or more buyers, by auction or otherwise. In someembodiments, data buyer server 118 may generally enable buyers toreview, bid on, or otherwise access the collected consumer information.In some embodiments, the collected consumer information may be audiencedata used by audience volume prediction server(s) 106 and/or audiencecorrelation server(s) 108.

System 100 may also include data buyer server 118 that enables a databuyer or potential data buyer to use services provided by audiencevolume prediction server(s) 106 and/or audience correlation server(s)108. In some embodiments, a user of data buyer server 118 may accessclient application(s) installed on data buyer server 118 to accessservices provided by audience volume prediction server(s) 106 and/oraudience correlation server(s) 108, as discussed further herein. In someembodiments, a user of data buyer server 118 may access remote and/ornetwork application(s) hosted on audience volume prediction server(s)106 and/or audience correlation server(s) 108 to access services.

Example Client Device

FIG. 2 shows an example mobile device 200, according to an embodiment ofthe claimed invention. In one embodiment, mobile device 200 is a mobileclient device, such as a laptop computer. Another example of a mobiledevice is a PDA or a cellular telephone that is arranged to send andreceive voice communications and messages such as SMS messages via oneor more wireless communication interfaces. Oftentimes, mobile electronicdevices will be capable of personal communication by connecting to oneor more wireless networks, connecting to multiple nodes of a singlewireless network, communicating over one or more channels to one or morenetworks, or otherwise engaging in one or more communication sessions.Generally, mobile device 200 may comprise any mobile or stationaryelectronic device. Such devices include personal computers, laptops,palmtops, PDAs, handheld computers, cellular telephones, smart phones,pagers, radio frequency (RF) devices, infrared (IR) devices, integrateddevices combining one or more of the preceding devices, and the like.Mobile device 200 may also comprise other electronic devices such asmultiprocessor systems, microprocessor-based or programmable consumerelectronics, network PCs, wearable computers, and the like.

Mobile device 200 may include many more, or fewer, components than thoseshown in FIG. 2. However, the components shown are sufficient todisclose an illustrative embodiment for practicing the presentinvention. As shown in the figure, mobile device 200 includes a centralprocessing unit (CPU) 222 in communication with a mass memory 230 via abus 224.

Mass memory 230 may include RAM 232, a ROM 234, and other storage means.Mass memory 230 illustrates an example of computer storage media forstorage of information such as computer readable instructions, datastructures, program modules or other data. Mass memory stores a basicinput/output system (“BIOS”) 240 for controlling low-level operation ofclient device 200. The mass memory also stores an operating system 241for controlling the operation of mobile device 200. It will beappreciated that this component may include a general purpose operatingsystem such as a version of Windows®, UNIX, or LINUX®, or a specializedmobile communication operating system such as Windows Mobile™, theSymbian® operating system, or the like. The operating system mayinclude, or interface with a Java® virtual machine module that enablescontrol of hardware components and/or operating system operations viaJava application programs.

Memory 230 further includes one or more data storage units 242, whichcan be utilized by mobile device 200 to store data used by variousprograms, applications, software modules, and the like. Mass memory 230may also include programs, applications, and/or software modules.Browser 244 may run under the control of operating system 241 totransmit, receive, render, and/or otherwise process documents of variousformats (e.g. PDF, Word, Excel, and the like), markup pages such as HTMLpages, XML pages, WAP pages (sometimes referred to as WAP cards), andthe like, and/or multimedia content (e.g., audio, video, graphics), andany other form of content deliverable over the web.

Mass memory 230 may also include an audience volume prediction client246 that enables a user to access audience volume predictionfunctionality of the claimed invention, provided by audience volumeprediction server(s) 106, described further herein. In some embodiments,mass memory 230 may also include an audience correlation client thatenables a user to access audience correlation functionality provided byaudience correlation server(s) 108. In some embodiments, a user ofmobile device 200 may access audience volume prediction functionalityand/or audience correlation functionality through browser 244, byaccessing web services hosted by audience volume prediction server(s)106 and/or audience correlation server(s) 108. In some embodiments,access to functionality of the audience volume prediction server(s)and/or audience correlation server(s) may be provided to the client viaan application program interface (API). A data buyer and/or third partymay create one or more applications that employ the API to access theprediction and/or correlation functionality. Mass memory 230 may alsoinclude other applications 250.

Mobile device 200 may also include a processor readable storage media228. Processor readable storage media may include volatile, nonvolatile,removable, and non-removable media implemented in any method ortechnology for storage of information, such as computer- orprocessor-readable instructions, data structures, program modules, orother data. Examples of processor readable storage media include RAM,ROM, EEPROM, flash memory or other memory technology, CD-ROM, digitalversatile disks (DVD) or other optical storage, magnetic cassettes,magnetic tape, magnetic disk storage or other magnetic storage devices,or any other media which can be used to store the desired informationand which can be accessed by a computing device. Processor readablestorage media may also be referred to herein as computer readablestorage media.

Mobile device 200 also includes a power supply 226, one or more wirelessinterfaces 260, an audio interface 262, a display 264, a keypad 266, anilluminator 268, an input/output interface 272, an optional hapticinterface 270, and an optional global positioning systems (GPS) receiver274. Power supply 226 provides power to mobile device 200. Arechargeable or non-rechargeable battery may be used to provide power.The power may also be provided by an external power source, such as anAC adapter or a powered docking cradle that supplements and/or rechargesa battery.

Mobile device 200 may optionally communicate with a base station, ordirectly with another mobile device. Wireless interface 260 may includecircuitry for coupling mobile device 200 to one or more wirelessnetworks, and is constructed for use with one or more communicationprotocols and technologies including, but not limited to, TCP/IP, UDP,GSM, CDMA, TDMA, SMS, GPRS, WAP, UWB, IEEE 802.16 (WiMax), and the like.

Audio interface 262 is arranged to produce and/or receive audio signalssuch as the sound of a human voice, music, and the like. For example,audio interface 262 may be coupled to a speaker and microphone (notshown) to enable telecommunication with others and/or generate an audioacknowledgement for some action. Display 264 may be a liquid crystaldisplay (LCD), gas plasma, light emitting diode (LED), or any other typeof display used with a client device. Display 264 may also include atouch sensitive screen arranged to receive input from an object such asa stylus or a digit from a human hand.

Keypad 266 may comprise any input device arranged to receive input froma user. For example, keypad 266 may include a keyboard, a push buttonnumeric dial, or the like. Keypad 266 may also include command buttonsthat are associated with selecting and performing changeable processes.Illuminator 268 may provide a status indication and/or provide light.Illuminator 268 may remain active for specific periods of time or inresponse to events. For example, when illuminator 268 is active, it maybacklight the buttons on keypad 266 and stay on while the client deviceis powered. Also, illuminator 268 may backlight these buttons in variouspatterns when particular actions are performed, such as dialing anotherclient device. Illuminator 268 may also cause light sources positionedwithin a transparent or translucent case of the mobile device toilluminate in response to actions.

Client device 200 may also include input/output interface 272 forcommunicating with external devices, such as a headset, or other inputor output devices not shown in FIG. 2. Input/output interface 272 canutilize one or more communication technologies, such as USB, infrared,Bluetooth™, and the like. Optional haptic interface 270 is arranged toprovide tactile feedback to a user of the client device. For example,the haptic interface may be employed to vibrate client device 200 in aparticular way when another user of a client device is calling.

Optional GPS transceiver 274 can determine the physical coordinates ofclient device 200 on the surface of the Earth, which typically outputs alocation as latitude and longitude values. GPS transceiver 274 can alsoemploy other geo-positioning mechanisms, including, but not limited to,triangulation, assisted GPS (AGPS), Enhanced Observed Time Difference(E-OTD), cell identifier (CI), service area identifier (SAT), enhancedtiming advance (ETA), base station subsystem (BSS), or the like, tofurther determine the physical location of client device 200 on thesurface of the Earth. It is understood that under different conditions,GPS transceiver 274 can determine a physical location within millimetersfor client device 200; and in other cases, the determined physicallocation may be less precise, such as within a meter or significantlygreater distances.

Example Network Device

FIG. 3 shows one embodiment of a network device, according to oneembodiment of the invention. Network device 300 may include many more,or fewer, components than those shown. The components shown, however,are sufficient to disclose an illustrative embodiment for practicing theinvention. Network device 300 may represent, for example, audiencevolume prediction server(s) 106, audience correlation server(s) 108,client devices (e.g. desktop personal computers) such as client device102, content server 116, and/or data buyer 118 of FIG. 1.

As shown in FIG. 3, network device 300 includes a CPU 322 incommunication with a mass memory 330 via a bus 324. Mass memory 330 mayinclude RAM 332, a ROM 334, and other storage means. Mass memory 330illustrates an example of computer storage media for storage ofinformation such as computer readable instructions, data structures,program modules or other data. Mass memory stores a basic input/outputsystem (“BIOS”) 340 for controlling low-level operation of networkdevice 300. The mass memory also stores an operating system 341 forcontrolling the operation of network device 300. It will be appreciatedthat this component may include a general purpose operating system suchas a version of Windows®, UNIX, or LINUX®, or a specialized mobilecommunication operating system such as Windows Mobile™, the Symbian®operating system, or the like. The operating system may include, orinterface with a Java® virtual machine module that enables control ofhardware components and/or operating system operations via Javaapplication programs.

Memory 330 further includes one or more data storage units 342, whichcan be utilized by network device 300 to store programs, applications,software modules, and the like, as well as the data used by suchprograms, applications, and/or software modules. Programs may comprisecomputer executable instructions which can be executed by network device300. Programs in mass memory 330 may include a browser application 343.Browser 343 may run under the control of operating system 341 totransmit, receive, render, and/or otherwise process documents of variousformats (e.g. PDF, Word, Excel, and the like), markup pages such as HTMLpages, XML pages, WAP pages (sometimes referred to as WAP cards), andthe like, and/or multimedia content (e.g., audio, video, graphics), andany other form of content deliverable over the web. Mass memory 330 mayalso include an audience volume prediction module 344 that enablesaudience volume prediction functionality of the claimed invention,provided by audience volume prediction server(s) 106, described furtherherein. In some embodiments, mass memory 330 may also include anaudience correlation client that enables audience correlationfunctionality provided by audience correlation server(s) 108. Massmemory 330 may also include other applications 348. Other examples ofapplication programs include content management applications, messagingapplications, schedulers, calendars, web services, transcoders, databaseprograms, word processing programs, spreadsheet programs, and so forth.Accordingly, programs may process images, audio, video, or markup pages,enable telecommunication with another user of another electronic device,and/or other services.

Server device 300 also includes an input/output interface 360 forcommunicating with input/output devices such as a keyboard, mouse,wheel, joy stick, rocker switches, keypad, printer, scanner, and/orother input devices not specifically shown in FIG. 3. A user of serverdevice 300 can use input/output devices to interact with a userinterface that may be separate or integrated with operating system 341,and/or programs in memory 330. Interaction with the user interfaceincludes visual interaction via a display, and a video display adapter354.

Server device 300 may include a removable media drive 352 and/or apermanent media drive 354 for computer-readable storage media. Removablemedia drive 352 can comprise one or more of an optical disc drive, afloppy disk drive, tape drive, and/or any other type of removable mediadrive. Permanent or removable storage media may include volatile,nonvolatile, removable, and non-removable media implemented in anymethod or technology for storage of information, such as computerreadable instructions, data structures, program modules, or other data.Examples of computer storage media include a CD-ROM 355, digitalversatile disks (DVD) or other optical storage, magnetic cassettes,magnetic tape, magnetic disk storage or other magnetic storage devices,RAM, ROM, EEPROM, flash memory or other memory technology, or any othermedia which can be used to store the desired information and which canbe accessed by a computing device.

Removable media drive 352 and/or permanent media drive 356 may alsoinclude processor readable storage media. Processor readable storagemedia may include volatile, nonvolatile, removable, and non-removablemedia implemented in any method or technology for storage ofinformation, such as computer- or processor-readable instructions, datastructures, program modules, or other data. Examples of processorreadable storage media include RAM, ROM, EEPROM, flash memory or othermemory technology, CD-ROM, digital versatile disks (DVD) or otheroptical storage, magnetic cassettes, magnetic tape, magnetic diskstorage or other magnetic storage devices, or any other media which canbe used to store the desired information and which can be accessed by acomputing device. Processor readable storage media may also be referredto herein as computer readable storage media.

Via a network communication interface unit 350, server device 300 cancommunicate with a wide area network such as the Internet, a local areanetwork, a wired telephone network, a cellular telephone network, orsome other communications network, such as networks 120 and/or 130 inFIG. 1. Network communication interface unit 350 is sometimes known as atransceiver, transceiving device, network interface card (NIC), and thelike.

Example Operations

FIG. 4 illustrates a logical flow diagram generally showing anembodiment of a process 400 for tuning a predicted audience volume. Insome embodiments, this process may be implemented by and/or executed ona network device such as audience volume prediction server(s) 106 ofFIG. 1, via an application such as audience volume prediction module 344of FIG. 3.

After a start block, a query for audience volume prediction is receivedat block 402. In some embodiments, the query may be specified orotherwise provided by the user. However, the invention is not solimited, and the query may also be provided by an operator,administrator or other person controlling audience volume predictionserver(s). In some embodiments, the query may include one or morecategories of consumer data along with one or more Boolean operators.The specified categories of consumer data may be of various categorytypes, including but not limited to market categories, demographiccategories, location categories, season categories, and the like. Forexample, the user may specify a query of “location=California ANDmarket=SUV purchaser” to query for consumer data on purchasers of SUVswho live in California. As another example, the user may specify a queryof “location=California OR Oregon AND market=video game console” toquery for consumer data on purchasers of (or individuals who evinced aninterest in) video game consoles who live in California or Oregon. Insome embodiments, a query may include Boolean operators and/or weightedcategories. For example, a user may specify a query of“market=LuxuryCars (with 80% confidence) AND gender=Male (with 90%confidence). On receiving the user specified query, the query may bestored in mass memory.

The query received at block 402 may be received as part of a request fora real time prediction of an advertising audience volume over a futuretime period. Such a request may, in some embodiments, be received from auser. In some embodiments, the request may be received from anadministrator, operator, or other person in control of audience volumeprediction server(s). In some embodiments the request may also includethe future time period. In some embodiments, where the request does notinclude a future time period, a default future time period (e.g. onemonth in the future from a current date) may be employed. One or both ofthe query and the future time period may be editable by the user, and/orby the administrator, operator, or controller.

At block 404, a past time period of historical data for audience volumeprediction may be provided or otherwise determined. In some embodiments,such a past time period may be specified as a recency (e.g., the lastweek, the last month, the last year, and the like). In some embodiments,the past time period may be specified as a range of dates (e.g. Jan. 1,2010 through Jun. 30, 2010). In some embodiments, the past time periodof historical data may be set as a parameter or set of parameters by anoperator, administrator and/or manager for process 400. In someembodiments, the past time period of historical data may be receivedfrom and/or specified by a user of process 400 (e.g. a data buyerseeking an audience volume prediction). In some embodiments, the pasttime period may be editable by at least one of the user or theadministrator, operator, or controller.

At block 406, stored historical audience data may be retrieved based onthe query and/or past time period. In some embodiments, retrieval ofdata may be made from a database or other data store, such as datastorage 110 and/or data stored in mass memory of audience volumeprediction server(s) 106 of FIG. 1. In some embodiments, the historicalaudience data retrieved may be based on the past time period ofhistorical data determined at block 404. Moreover, in some embodiments,the historical audience data retrieved may include a plurality ofhistorical advertising audience volumes.

Some embodiments may support fast, real-time audience forecastingthrough the employment of a cluster of one or more computers. Such acluster may be employed to store relevant historical data in main memoryfor optimal (e.g. fast) access. In some embodiments, memory usage may beoptimize and/or cost minimized by configuring the computers such thatleast used portions of the data are stored on disk and brought to mainmemory and/or cache when they are accessed sufficiently frequently (e.g.above a certain threshold of number of accesses in a given period oftime). Further, for additional speed-up and/or optimization, dynamicand/or static (e.g. user-specified, algorithmically determined, and/orpre-specified) data sampling may be employed to provide audience volumepredictions of a certain pre-determined granularity and/or confidencelevel.

At block 408, a real time audience volume prediction for a future timeperiod may be generated and/or provided based on the retrieved audiencedata and the future time period. In some embodiments, the future timeperiod may be determined based on the past time period of historicaldata determined at block 404. For example, if the past time data ofhistorical data is six months (e.g. the last six months from the currenttime, or a specified range of dates that is six months long), then thefuture time period may also be six months. In some embodiments, thefuture time period may be related to the past time period by a scalefactor or a more complex mathematical function. For example, the futuretime period may be specified as 1.5 times the past time period. In someembodiments, a scaling factor may be used to adjust for a knownseasonality effect; e.g., such as the effect that in May people are 1.5times more likely to be interested in pool cleaning and otherwarm-weather-related products or services.

In some embodiments, the audience volume prediction is provided to theuser as a number of persons (e.g. web users, consumers, potentialconsumers, and the like) that will be reached by an advertising campaigntargeting persons according to the user specified query. Such anaudience volume prediction may be specified as an exact number or as anapproximate estimate of a number of persons. In some embodiments, theaudience volume prediction may be provided as an estimated range of thenumber of persons in the predicted audience volume (e.g. from 10,000 to20,000 persons). In some embodiments, such ranges may be determined as arange of forecasts that satisfies a pre-determined statisticalconfidence interval that may be specified by a user and/or anadministrator of the system.

In some embodiments, generating the real time prediction for the futuretime period may be based on an evaluation of the query over thehistorical audience data retrieved at block 406. Such evaluation mayinclude applying the query to the historical audience data to generate asubset of the data that corresponds to the query. In some embodiments,generating the real time prediction may include further analysis of thehistorical audience data.

FIG. 5 provides an example of the analysis that may be performed todetermine the audience volume prediction. After a start block, process500 may proceed to block 502 where one or more first weights may bedetermined for the stored audience data based on recency and on aselectable scaled smoothing. In some embodiments, the first weights maybe weighting factors that determine the weights given to the variousdata when calculating the predicted audience volume. In someembodiments, more recent data may be weighted for heavily than olderdata. For example, data collected in the last month may be weighted moreheavily than data collected in the previous month, and so forth, as inthe following table.

Time period for collected data Weighting factor (w) One month ago untilcurrent time w = 1.0 Two months ago until one month w = 0.8 ago Threemonths ago until two months w = 0.6 ago Four months ago until threemonths w = 0.4 ago Five months ago until four months w = 0.2 ago

In this example, historical data is retrieved up until five months fromthe current time. In some embodiments, weighting based on recency may beconsidered a recency-based averaging of the historical data, and mayfunction as a smoothing to account for more-or-less extreme changes inthe rate of collection of historical data.

At block 504, a further N number of weights may be determined for thestored audience data based on other factors and on a selectable scaledsmoothing. Such other factors may include but are not limited to: day ofthe week (e.g. data collected Saturday and Sunday is weighted differentthan data collected on weekdays), seasonality (e.g. data collected inthe winter is weighted differently than data collected in the summer),special events (e.g. weighting related to holidays, natural disasters,entertainment events, and the like), and/or geographical factors (e.g.different weights for southern U.S. vs. eastern U.S.). In someembodiments, weighting may also be based on the source of the particularconsumer data collected. For example, data collected from web site X maybe weighted differently than data collected from web site Y.

At block 506, the predicted audience volume may be determined for thefuture time period based on combined weights for the stored audiencedata. In some embodiments, this determination may be performed through acalculation according to a particular algorithm. For example, predictedaudience volume (PAV) may be calculated through a linear sum of weighteddata:PAV=p(1)*w(1)+p(2)*w(2)+p(3)*w(3)+ . . . p(n)*w(n)

where p(i) represents the historical data being analyzed and w(i)represents one or more weight factors applied to the particular data.

Although this example shows a linear sum, other algorithms may becontemplated without departing from the spirit or scope of theinvention. For example, a quadratic algorithm or other polynomialexpansion may be employed, and/or exponential, logarithmic, or virtuallyany other type of mathematical algorithm.

In some embodiments, the weights applied for recency and/or otherfactors may be non-uniform (e.g. different weights applied to differentfactors) and may be selectable by the user and/or by an implementer,operator and/or administrator of the audience volume prediction server.

After the predicted audience volume has been determined, it may beprovided to the user via a report screen or other means (described inmore detail with regard to FIG. 7). In some embodiments, the predictedaudience volume may be provided to the user as a number of persons thatare predicted to be reached by the specified query for the determinedfuture time period, and/or a range of an estimated number of personspredicted to be reached. In some embodiments, retrospective (e.g.historical) information may further be provided to the user. Forexample, the user may be provided with data specifying that a campaignaccording to the user specified query would have reached X persons hadit been run during the past time period. Following block 506, process500 may return.

In some embodiments, a confidence metric may be determined for thepredicted audience volume. Such a confidence metric may be calculated asa percentage level of confidence (e.g. that the audience prediction isaccurate to some threshold of accuracy such as 90%, 95%, and the like).In some embodiments, the confidence metric may be presented as amathematical variance, standard deviation, sigma-level confidence, andthe like. In some embodiments, the confidence metric may be presented ina subjective and/or descriptive manner that may be more readilyunderstandable by the user (e.g. high confidence, medium confidence, lowconfidence). The confidence metric may be presented to the user alongwith the predicted audience via a report screen or other means (e.g. theuser interface depicted in FIG. 7).

Returning to FIG. 4, a determination is made at decision block 410whether the predicted audience volume is to be tuned. In someembodiments, this determination may be based on an indication from theuser that the predicted audience volume is to be tuned. If so, at block412 the user may be enabled to tune the query for audience volumeprediction. Moreover, in embodiments where the user is enabled tospecify the past time period for the retrieved and analyzed historicalaudience data, the user may further be enabled to tune the past timeperiod (e.g. specify a different time period) at block 412. In someembodiments, the user may further be enabled to tune the future timeperiod.

FIG. 6 provides a more detailed example of tuning of the query foraudience volume prediction. After a start block process 600 proceeds toblock 602, where a determination is made to tune based on one or morein-market (e.g. market) type categories in the user-specified query. Forexample, market type categories may categories for a consumer's purchaseof and/or interest in goods and services related to travel, finance,retail purchases, automotive purchases, and virtually any other type ofgood or service. At block 602, the user may edit the query to change,add or remove in-market categories.

At block 604, a determination is made to tune based on one or moredemographic type categories. Demographic type categories generallyinclude categories associated with virtually demographic factor,including for example age and/or gender. At block 604, the user may editthe query to change, add or remove demographic categories.

At block 606, a determination is made to tune based on one or morelocation type categories. Location type categories generally includecategories associated with geographic locations (e.g. continent,country, state, province, prefecture, county, city, neighborhood,address, and the like). At block 606, the user may edit the query tochange, add or remove location categories.

At block 608, a determination is made to tune based on one or moreseasonal type categories (e.g. seasons of the year). Season typecategories may also include particular holiday seasons (e.g. apredetermined period of time prior to a holiday such as Christmas).Season type categories may also generally include categories related toparticular time periods (e.g. months, weeks, days of the year,particular days of the week, and the like). At block 608, the user mayedit the query to change, add or remove season type categories.

At block 610, a determination is made to tune based on other types ofcategories. In addition to editing the particular categories included ina query, the user may be enabled to change the Boolean operators used tocombine the categories to form the query. For example, a user specifiedquery of “location=California AND gender=male” may be tuned to“location=California OR gender=male”. After block 610, process 600 mayreturn.

Following the user's tuning of the query, process 400 may then return to402 and repeat the process to regenerate the prediction of theadvertising audience volume. In this way, process 400 may execute overone or more iterations during which the user specifies a query and apredicted audience volume is determined based on the query and the pasttime period. Such iterations may continue until the user is satisfiedwith the predicted audience volume, until a predetermined number ofiterations have been executed, or until some other termination criterionor set of criteria is satisfied. At decision block 410, if the predictedaudience volume is not to be tuned, process 400 may return.

In some embodiments, the audience volume prediction functionality isprovided in real-time, such that the user is provided with an audiencevolume prediction within a certain period of time followingspecification of the query. In some embodiments, such period of time maybe brief (e.g. more or less in real time from the perspective of theuser). In some embodiments, the real-time provision of the audiencevolume prediction may include parallel processing of the retrieval ofhistorical data and/or analysis of historical data by multiple serversin a cluster of audience volume prediction servers. In some embodiments,a level of service and/or quality of service may be used to determine anumber of servers to be used for parallel processing of historical dataand/or to make the real time prediction of audience volume. Such levelof service and/or quality of service may be specified by the user and/orby an administrator, operator, controller, or the like. For example, ifa user specifies a level of service that is a one second response timeto a query, a certain number of servers may be allocated to process thatrequest to achieve that level of service. Some speed up techniques maybe determined by an implementer, operator, and/or administrator of theclaimed invention to achieve a predetermined level of service and/orsatisfy the real time quality of service requirements of the user.

In some embodiments, real time results may be achieved through memorymanagement techniques employed on the one or more audience volumeprediction servers and/or data storage. For example, infrequentlyaccessed historical data may be stored on hard disk with slower access,and more frequently accessed historical data may be stored in memorywith faster access. In this way, speed-up of processing may be achievedat lower cost (e.g. a certain result achieved using 1000 servers holdingdata in memory may be achieved with a substantially similar level ofservice using 10 servers storing at least a portion of the needed dataon hard disk).

In some embodiments, real time results may be achieved throughtechniques that speed up the processing at the data retrieval and/oranalysis phases of the process. For example, retrieval of historicaldata may include a sampling of the historical data instead of retrievalof a full data set. Further, analysis of the historical data may includeanalysis of a sample (e.g. subset) of the retrieved historical data.Sampling may be employed in such a way that the sample of data is arepresentative sample of a more complete historical data set, to ensurea result that is substantially similar to the result that would beachieved based on the more complete historical data set. In someembodiments, sampling may be performed to meet a user-specified level ofserver and/or quality of service. For example, to meet a level ofservice that is a one second response time, it may be necessary toretrieve and/or analyze 50% of the historical audience data.

In this way, level of service (e.g. response time), quality of service(e.g. accuracy and/or confidence level of result), and/or cost (e.g.number of servers or other resources employed) may be weighed and/orbalanced against one another to ensure an appropriate level of serviceto the user. For example, a faster response time may require increasedsampling of the historical data set, which may in turn lead to a lowerconfidence level of the result. In such circumstances, if both a fastresponse time and a high confidence level are required, additionalservers may be employed to ensure a fast response time with a highconfidence level. The cost of additional servers may be passed on to theuser in the form of higher service fees and the like.

FIG. 7 illustrates an example user interface for audience volumeprediction and/or calculation. Such a user interface may providefunctionality that allows a user to specify a query for audience volumeprediction, and provide in real-time an audience volume prediction for afuture time period based on that query as well as a summary ofhistorical audience volume for the specified query. In some embodiments,such a user interface may be provided to a user who is using data buyerserver 118 and/or one or more of client devices 102-104 shown in FIG. 1.In some embodiments, such a user interface may be implemented through aset of APIs that are provided to the user from a server device such asaudience volume prediction server(s) 106 shown in FIG. 1.

As shown in FIG. 7, user interface 700 includes various controls,dialogs, and other user interface elements to enable a user to specify aquery for audience volume prediction. These may include a “Select Type”control 702 that enables a user to select one or more types ofcategories to include in the user-specified query. Such types ofcategories may include, but are not limited to, In-Market, Geographic,Demographic, Frequent Buyers, Custom Categories, Interest, Branded Data,Business-to-business (B2B), and the like. Selection of a category typethrough control 702 may include selection of a radio button or othercontrol-type. In the example shown, the user has selected theDemographic category type. In some embodiments, control 702 may alsoinclude a dialog or other control to enable the user to search forcategories and/or category types.

User interface 700 may further include a “Select Categories” control704. In some embodiments, control 704 may present a list of categoriesbased on the user selection of category type through control 702. In theexample shown, the user has selected the Demographic category typethrough control 702, and control 704 has been populated with a list ofcategories that correspond to the Demographic category type (e.g.,categories for Age, Citizenship, Education, Employment, and the like).The user may then select one or more categories from this list to beincluded in the query. In some embodiments, categories may be presentedin a hierarchical structure as a listing of categories, sub-categories,sub-sub-categories, and so forth. Such a hierarchy of categories may bepresented to the user in a tree structure or the like. For example, asshown in the figure, the user has expanded the Gender category (e.g.through use of a +/− expand/collapse tree control) to expose twosub-categories of Gender—Male and Female. The user has further selectedthe Female sub-category for inclusion in the query.

User interface 700 may also include elements that display theuser-specified query as the user adds, removes and/or otherwise modifiesthe query. For example, elements 708 and 710 may depict two exemplarycategories that the user has selected to be included in the query,through use the controls 702 and 704. Element 708 shows that the userhas selected a first category of “Luxury Cars”, specified in itshierarchical form as category type “In-Market” combined with category“Autos>By Class>Luxury Cars”. Element 710 shows that the user hasfurther selected a second category of “Female”, specified in itshierarchical form as category type “Demographic” combined with category“Gender>Female”. In some embodiments, user interface 700 may alsopresent a category size for selected categories. Such category size maybe based on an analysis of historical data, for example a determinationthat the specified category would have reached an audience volume of acertain number during a specified past time period (e.g. the lastmonth). For example, element 708 includes a determined Category Size of7,000,000 and element 710 includes a determined category size of50,000,000. User interface 700 may further include a control 712 toallow a user to specify a logical Boolean operator as part of thespecified query. For example, FIG. 7 depicts control 712 as set to “AND”by the user, to specify that the query should be a first category AND asecond category. Elements 708 and 710 may further include “delete”controls as shown, to enable the user to delete a particular categoryfrom the query.

User interface 700 may also include a control 714 to enable the user toadd one or more additional categories (e.g. sub-segments) to the query.Addition of further categories to the query may cause the user interfaceto display the additionally specified categories in additionalcategories elements such as elements 708 and 710. In the way, the useris able to specify a custom query composed of categories and/orsubcategories, combined using logical operators.

Once the user has specified the query, element 706 may display a currentreach for the specified query. In some embodiments, this current reachmay be a historical audience volume corresponding to the user-specifiedquery, provided as a retrospective analysis to the user. For example, asshown in FIG. 7, element 706 shows a current reach of 1,000,000 audiencevolume, indicating that a campaign using the user-specified query wouldhave reached an audience volume of 1,000,000 if it had been run during apast time period (e.g. the last month).

Element 706 may further display “Estimated Impressions” based on anaudience volume prediction for a future time period. The predictedaudience volume may be provided to the user as a number of persons thatare predicted to be reached by a campaign that uses the specified queryfor a determined future time period, and/or a range of an estimatednumber of persons predicted to be reached. Such a prediction may bebased on an analysis of stored historical data, as described herein withregard to FIG. 4. For example, as shown in FIG. 7, element 706 includes“Estimated Monthly Impressions” as a range of 600,000 to 1,200,000,indicating that a campaign using the user-specified query is estimatedto reach a predicted audience volume within this range if run during acertain future time period. In some embodiments, the “Current Reach” and“Estimated Impressions” number may be provided and/or updated to theuser in real-time as the user specifies and/or modifies the queryrespectively, such that the user may more or less immediately see thepredicted audience volume and/or retrospective audience volume thatwould be reached by a campaign using the query.

It should be noted that user interface 700 is an example user interfacethat may be employed in embodiments of the invention. Generally, such auser interface may include more or fewer elements that those depicted,without departing from the spirit or scope of the invention. Forexample, though not depicted in FIG. 7, user interface 700 may furtherinclude controls to allow the user to edit the past time period and/orfuture time period.

It will be understood that figures, and combinations of steps in theflowchart-like illustrations, can be implemented by computer programinstructions. These program instructions may be provided to a processorto produce a machine, such that the instructions executing on theprocessor create a means for implementing the actions specified in theflowchart blocks. The computer program instructions may be executed by aprocessor to cause a series of operational steps to be performed by theprocessor to produce a computer implemented process for implementing theactions specified in the flowchart block or blocks. These programinstructions may be stored on a machine readable media, such as computerreadable media and/or processor readable storage media.

The above specification, examples, and data provide a completedescription of the manufacture and use of the composition of theinvention. Since many embodiments of the invention can be made withoutdeparting from the spirit and scope of the invention, the inventionresides in the claims hereinafter appended.

What is claimed as new and desired to be protected by Letters Patentis:
 1. A computer implemented method for generating informationregarding an advertising audience volume, comprising using a processorto perform actions over a network, the actions comprising: processing arequest for a real time prediction that forecasts the advertisingaudience volume over an editable future time period, the requestincluding at least a query, wherein the request identifies the editablefuture time period and wherein the request corresponds to a specifiedlevel of service and quality of service that determines a number ofprediction servers for processing the request; managing, at a predictionserver, a speed of processing the request at least by storing a portionof historical data in memory and another portion of the historical datain one or more persistent storage devices based in part or in whole uponfrequencies of accessing the historical data; evaluating the query onthe historical data over the one or more past time periods, by:identifying historical advertising audience volumes that would have beenreached had the query been executed during the one or more past timeperiods; retrieving a set of historical data by sampling the historicaladvertising audience volumes for reducing an amount of the historicaldata for processing the request, wherein a level of service and aquality of service are balanced against one another by changing at leasta sampling rate for sampling the historical advertising audience volumesand a number of employed servers to satisfy the user specified level ofservice and quality of service; and performing smoothing on the set ofhistorical data retrieved from the historical advertising audiencevolumes based at least in part upon one or more data sources for the setof historical data and temporal differences in the one or more past timeperiods; and generating the real time prediction for the editable futuretime period based at least on the evaluation of the query over thehistorical data for the one or more past time periods.
 2. The method ofclaim 1, further comprising at least one of: weighting at least aportion of the editable future time period and the one or more past timeperiods; or weighting at least a portion of the query.
 3. The methodclaim 1, further comprising: applying at least one edit to the query,the editable future time period, or the one or more past time periods;and regenerating the real time prediction of the advertising audiencevolume based in part or in whole upon the at least one edit to thequery, the editable future time period, or the one or more past timeperiods.
 4. The method of claim 1, wherein generating the real timeprediction for the editable future time period further includes:determining a plurality of weights for the historical advertisingaudience volumes based at least on recency of data in the historicaladvertising audience volumes; and determining the real time predictionfor the editable future time period based on applying the plurality ofweights to the historical advertising audience volumes.
 5. The method ofclaim 1, wherein generating the real time prediction for the editablefuture time period further includes: determining a plurality of weightsfor the historical advertising audience volumes based at least onrecency of data in the historical advertising audience volumes; anddetermining the real time prediction for the editable future time periodbased on applying the plurality of weights to the historical advertisingaudience volumes in at least one of a mathematical calculation and alogic determination.
 6. The method of claim 1, further comprising:applying at least one edit to the query, wherein the at least one editto the query includes a tuning of at least one category of data includedin the query.
 7. The method of claim 1, further comprising: applying atleast one edit to the query, wherein the at least one edit to the queryincludes a tuning of at least one category of data included in thequery, wherein the at least one category is associated with a categorytype that is at least one of in-market type, demographic type, locationtype and season type.
 8. The method of claim 1, wherein the real timeprediction of the advertising audience volume includes at least a numberof persons in the predicted advertising audience volume or a range ofthe number of persons in the predicted advertising audience volume. 9.The method of claim 1, wherein changing at least the sampling rate forsampling the historical advertising audience volumes to satisfy thespecified level of service and quality of service further comprisesdetermining a subset of the historical data to be sampled based at leaston meeting the specified level of service or the quality of service. 10.The method of claim 1, further comprising determining a confidencemetric for the real time prediction.
 11. The method of claim 1, whereingenerating the real time prediction for the editable future time periodfurther includes: determining at least one subset of the historicaladvertising audience volumes; and evaluating the query over thedetermined at least one subset of the historical advertising audiencevolumes for the one or more past time periods.
 12. One or more processorreadable non-transitory computer-readable storage media that includesinstructions, which when executed by at least one processor, cause theat least one processor to perform a set of acts, the set of actscomprising: processing a request for a real time prediction thatforecasts the advertising audience volume over an editable future timeperiod, the request including at least a query, wherein the requestidentifies the editable future time period and wherein the requestcorresponds to a specified level of service and quality of service thatdetermines a number of prediction servers for processing the request;managing, at a prediction server, a speed of processing the request atleast by storing a portion of historical data in memory and anotherportion of the historical data in one or more persistent storage devicesbased in part or in whole upon frequencies of accessing the historicaldata; evaluating the query on the historical data over the one or morepast time periods, by: identifying historical advertising audiencevolumes that would have been reached had the query been executed duringthe one or more past time periods; retrieving a set of historical databy sampling the historical advertising audience volumes for reducing anamount of the historical data for processing the request, wherein alevel of service and a quality of service are balanced against oneanother by changing at least a sampling rate for sampling the historicaladvertising audience volumes and a number of employed servers to satisfythe specified level of service and quality of service; and performingsmoothing on the set of historical data retrieved from the historicaladvertising audience volumes based at least in part upon one or moredata sources for the set of historical data and temporal differences inthe one or more past time periods; and generating the real timeprediction for the editable future time period based at least on theevaluation of the query over the historical data for the one or morepast time periods.
 13. The media of claim 12, the set of acts furthercomprising enabling at least a portion of the editable future timeperiod and the one or more past time periods to be weighted.
 14. Themedia of claim 12, the set of acts further comprising enabling at leasta portion of the query to be weighted.
 15. The media of claim 12,wherein generating the real time prediction for the editable future timeperiod further includes: determining a plurality of weights for thehistorical advertising audience volumes based at least on recency ofdata in the historical advertising audience volumes; and determining thereal time prediction for the editable future time period based onapplying the plurality of weights to the historical advertising audiencevolumes.
 16. The media of claim 12, wherein generating the real timeprediction for the editable future time period further includes:determining a plurality of weights for the historical advertisingaudience volumes based at least on recency of data in the historicaladvertising audience volumes; and determining the real time predictionfor the editable future time period based on applying the plurality ofweights to the historical advertising audience volumes in at least oneof a mathematical calculation and a logic determination.
 17. The mediaof claim 12, the set of acts further comprising: applying at least oneedit to the query, wherein the at least one edit to the query includes atuning of at least one category of data included in the query.
 18. Themedia of claim 12, the set of acts further comprising: applying at leastone edit to the query, wherein the at least one edit to the queryincludes a tuning of at least one category of data included in thequery, wherein the at least one category is associated with a categorytype that is at least one of in-market type, demographic type, locationtype and season type.
 19. The media of claim 12, wherein the real timeprediction of the advertising audience volume includes at least a numberof persons in the predicted advertising audience volume or a range ofthe number of persons in the predicted advertising audience volume. 20.The media of claim 12, wherein changing at least the sampling rate forsampling the historical advertising audience volumes to satisfy thespecified level of service and quality of service further comprisesdetermining a subset of the historical data to be sampled based at leaston meeting the specified level of service or the quality of service. 21.The media of claim 12, further comprising determining a confidencemetric for the real time prediction.
 22. The media of claim 12, whereingenerating the real time prediction for the editable future time periodfurther includes: determining at least one subset of the historicaladvertising audience volumes; and evaluating the query over thedetermined at least one subset of the historical advertising audiencevolumes for the one or more past time periods.
 23. A system forgenerating information regarding an advertising audience volume over anetwork, comprising: a server device that performs actions, the actionsincluding: processing a request for a real time prediction thatforecasts the advertising audience volume over an editable future timeperiod, the request including at least a query, wherein the requestidentifies the editable future time period and wherein the requestcorresponds to a specified level of service and quality of service thatdetermines a number of prediction servers for processing the request;managing, at a prediction server, a speed of processing the request atleast by storing a portion of historical data in memory and anotherportion of the historical data in one or more persistent storage devicesbased in part or in whole upon frequencies of accessing the historicaldata; evaluating the query on the historical data over the one or morepast time periods, by: identifying historical advertising audiencevolumes that would have been reached had the query been executed duringthe one or more past time periods; retrieving a set of historical databy sampling the historical advertising audience volumes for reducing anamount of the historical data for processing the request, wherein alevel of service and a quality of service are balanced against oneanother by changing at least a sampling rate for sampling the historicaladvertising audience volumes and a number of employed servers to satisfythe specified level of service and quality of service; and performingsmoothing on the set of historical data retrieved from the historicaladvertising audience volumes based at least in part upon one or moredata sources for the set of historical data and temporal differences inthe one or more past time periods; and generating the real timeprediction for the editable future time period based at least on theevaluation of the query over the historical data for the one or morepast time periods.
 24. The system of claim 23, the server device furtherperforming actions including: enabling at least a portion of theeditable future time period and the one or more past time periods to beweighted.
 25. The system of claim 23, the server device furtherperforming actions including: enabling at least a portion of the queryto be weighted.
 26. The system of claim 23, wherein generating the realtime prediction for the editable future time period further includes:determining a plurality of weights for the historical advertisingaudience volumes based at least on recency of data in the historicaladvertising audience volumes; and determining the real time predictionfor the editable future time period based on applying the plurality ofweights to the historical advertising audience volumes.
 27. The systemof claim 23, wherein generating the real time prediction for theeditable future time period further includes: determining a plurality ofweights for the historical advertising audience volumes based at leaston recency of data in the historical advertising audience volumes; anddetermining the real time prediction for the editable future time periodbased on applying the plurality of weights to the historical advertisingaudience volumes in at least one of a mathematical calculation and alogic determination.
 28. The system of claim 23, the server devicefurther performing actions including: applying at least one edit to thequery, wherein the at least one edit to the query includes a tuning ofat least one category of data included in the query.
 29. The system ofclaim 23, the server device further performing actions including:applying at least one edit to the query, wherein the at least one editto the query includes a tuning of at least one category of data includedin the query, wherein the at least one category is associated with acategory type that is at least one of in-market type, demographic type,location type and season type.
 30. The system of claim 23, wherein thereal time prediction of the advertising audience volume includes atleast one of a number of persons in the predicted advertising audiencevolume or a range of the number of persons in the predicted advertisingaudience volume.
 31. The system of claim 23, wherein changing at leastthe sampling rate for sampling the historical advertising audiencevolumes to satisfy the specified level of service and quality of servicefurther comprises determining a subset of the historical data to besampled based at least on meeting the specified level of service or thequality of service.
 32. The system of claim 23, further comprisingdetermining a confidence metric for the real time prediction.
 33. Thesystem of claim 23, wherein generating the real time prediction for theeditable future time period further includes: determining at least onesubset of the historical advertising audience volumes; and evaluatingthe query over the determined at least one subset of the historicaladvertising audience volumes for the one or more past time periods. 34.An apparatus for generating information regarding an advertisingaudience volume over a network, comprising: a memory device for storingdata and instructions; and processor device that is configured toexecute instructions stored in the memory device, wherein executions ofthe instructions by the processor device cause the processor device to:process a request for a real time prediction that forecasts theadvertising audience volume over an editable future time period, therequest including at least a query, wherein the request identifies theeditable future time period and wherein the request corresponds to aspecified level of service and quality of service that determines anumber of prediction servers for processing the request; manage, at aprediction server, a speed of processing the request at least by storinga portion of historical data in memory and another portion of thehistorical data in one or more persistent storage devices based in partor in whole upon frequencies of accessing the historical data; evaluatethe query on the historical data over the one or more past time periods,by: identifying historical advertising audience volumes that would havebeen reached had the query been executed during the one or more pasttime periods; retrieving a set of historical data by sampling thehistorical advertising audience volumes for reducing an amount of thehistorical data for processing the request, wherein a level of serviceand a quality of service are balanced against one another by changing atleast a sampling rate for sampling the historical advertising audiencevolumes and a number of employed servers to satisfy the specified levelof service and quality of service; and perform smoothing on the set ofhistorical data retrieved from the historical advertising audiencevolumes based at least in part upon one or more data sources for the setof historical data and temporal differences in the one or more past timeperiods; and generate the real time prediction for the editable futuretime period based at least on the evaluation of the query over thehistorical data for the one or more past time periods.
 35. The apparatusof claim 34, wherein executions of the instructions by the processordevice further cause the processor device to: enable at least a portionof the editable future time period and the one or more past time periodsto be weighted.
 36. The apparatus of claim 34, wherein executions of theinstructions by the processor device further cause the processor deviceto: enable at least a portion of the query to be weighted.
 37. Theapparatus of claim 34, wherein generating the real time prediction forthe editable future time period further includes: determining aplurality of weights for the historical advertising audience volumesbased at least on recency of data in the historical advertising audiencevolumes; and determining the real time prediction for the editablefuture time period based on applying the plurality of weights to thehistorical advertising audience volumes.
 38. The apparatus of claim 34,wherein generating the real time prediction for the editable future timeperiod further includes: determining a plurality of weights for thehistorical advertising audience volumes based at least on recency ofdata in the historical advertising audience volumes; and determining thereal time prediction for the editable future time period based onapplying the plurality of weights to the historical advertising audiencevolumes in at least one of a mathematical calculation and a logicdetermination.
 39. The apparatus of claim 34, wherein executions of theinstructions by the processor device further cause the processor deviceto: apply at least one edit to the query, wherein the at least one editto the query includes a tuning of at least one category of data includedin the query.
 40. The apparatus of claim 34, wherein executions of theinstructions by the processor device further cause the processor deviceto: apply at least one edit to the query, wherein the at least one editto the query includes a tuning of at least one category of data includedin the query, wherein the at least one category is associated with acategory type that is at least one of in-market type, demographic type,location type and season type.
 41. The apparatus of claim 34, whereinthe real time prediction of the advertising audience volume includes atleast one of a number of persons in the predicted advertising audiencevolume or a range of the number of persons in the predicted advertisingaudience volume.
 42. The apparatus of claim 34, wherein changing atleast the sampling rate for sampling the historical advertising audiencevolumes to satisfy the specified level of service and quality of servicefurther comprises determining a subset of the historical data to besampled based at least on meeting the specified level of service or thequality of service.
 43. The apparatus of claim 34, wherein executions ofthe instructions by the processor device further cause the processordevice to: determine a confidence metric for the real time prediction.44. The apparatus of claim 34, wherein generating the real timeprediction for the editable future time period further includes:determining at least one subset of the historical advertising audiencevolumes; and evaluating the query over the determined at least onesubset of the historical advertising audience volumes for the one ormore past time periods.